Adam S. Butterworth

This study conducts the first genome-wide association study of the phosphoproteome, identifying genetic variants influencing protein phosphorylation and linking them to disease traits.

PEGASUS defines a community reporting standard for GWAS predicted effector genes: a metadata record, a full locus-by-gene evidence matrix, and a derived author-prioritised gene list.

This study identifies a BACH2-NRF2 axis regulating fetal hemoglobin activation through genetic and functional analyses in a large multi-ancestry cohort.

This study expands the human plasma proteome analysis using an enhanced SomaScan platform, identifying new genetic associations and disease mechanisms beyond the classical secretome.

This study uses large biobanks to identify gene-trait pairs with causal evidence, rediscover approved drug targets, and develop a predictive model for drug development.

This study identifies genetic determinants of plasma protein variance across ancestries and shows their impact on cardiometabolic disease risk.

This study evaluates deep-learning methods for improving polygenic scores, finding limited benefits due to confounding effects from linkage disequilibrium.

The paper introduces a method to improve genetic discovery and fine-mapping by analyzing latent factors from high-dimensional traits, showing enhanced power compared to traditional approaches.

This study identifies metabolic reaction fluxes that interact with CAD risk alleles, revealing their role as amplifiers or buffers in disease susceptibility.

This study identifies genetic determinants of blood gene expression and splicing, linking them to molecular phenotypes and health outcomes through QTL analysis and colocalization.

The paper identifies genetic regulators of 184 neuro-related proteins in human plasma and links them to disease traits and drug targets.

The paper identifies misexpression of inactive genes in whole blood and links it to nearby rare structural variants using RNA and genome sequencing data.

This study identifies 40 circulating proteins associated with cancer risk using MR and colocalization, highlighting potential therapeutic targets and adverse effects of protein-altering interventions.

This study identifies 1,289 genetic signals for type 2 diabetes in 2.5 million individuals, revealing distinct clusters linked to vascular complications and highlighting ancestry-specific genetic risk factors.

The paper identifies pQTLs for 91 plasma proteins, finds novel associations, and uses MR to uncover causal roles in immune-mediated diseases, suggesting new therapeutic targets.

The paper develops genetic scores for 17,227 molecular traits using multi-omic data from INTERVAL and validates them across ancestries, demonstrating their utility in pathway analysis and disease association discovery.

flashfmZoom is an R Shiny app that enables joint fine-mapping and exploration of GWAS results from the UK Biobank to identify shared or distinct causal variants across traits.

The paper develops genetically personalised organ-specific metabolic models to identify associations between metabolic fluxes and disease risk, finding 97 fluxes linked to coronary artery disease.

This study identifies 400+ genetic loci associated with circulating metabolic traits and highlights pleiotropy and novel disease pathways.

flashfm-ivis provides interactive visualizations for exploring shared and distinct causal variants across multiple quantitative traits using fine-mapping results.

This study identifies 17 genetic risk loci for spontaneous coronary artery dissection (SCAD), including 12 new ones, and highlights genes related to artery integrity and coagulation.

The paper reports LTL measurements in 474,074 UK Biobank participants, identifying factors like age, sex, ethnicity, and white cell count associated with LTL variation.

The paper uses polygenic scores to identify plasma proteins causally linked to cardiometabolic diseases and finds some are targets for existing therapies.

This study uses Mendelian randomization to identify plasma proteins associated with stroke risk and potential therapeutic targets.

The paper identifies genetic associations between blood lipid metabolites and cardiometabolic disease loci in South Asian populations using lipidomics data from two studies.

This study identifies 592 genetic loci associated with 184 plasma proteins in 26,494 individuals and finds causal relationships between protein levels and disease.

A coronary artery disease GWAS of 181,522 cases among 1,165,690 participants yields 241 associations (54 new); an eight-predictor consensus nominates 185 candidate causal genes, 94 with ≥3 predictors.

This study uses Mendelian randomization to examine if ACE inhibitors or cardiometabolic risk factors affect lung ACE2 and TMPRSS2 expression or plasma ACE2 levels, finding no overall association.

The paper characterizes the genetic structure of South Asian populations, identifies founder effects and high homozygosity, and develops a custom SNP array for genetic studies in the region.

This paper uses Mendelian randomization and colocalization to identify causal relationships between plasma proteins and disease phenotypes, finding 111 putatively causal effects.